代码搜索:Multiplicative

找到约 131 项符合「Multiplicative」的源代码

代码结果 131
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c inverse.c

/* Author: Pate Williams (c) 1997 2.142 Algorithm Computing multiplicative inverses in Zn See "Handbook of Applied Cryptography" by Alfred J. Menezes et al page 71. */ #include
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shortestpaths

Shortest Paths with Multiplicative Cost. In a given undirected graph, the path cost is measured as a product of all the edges in the path. The weights are rational numbers (e.g., 0.25, 0.75, 3.75 et
www.eeworm.com/read/433223/8538635

m gemanova.m

function model = gemanova(X,F,Fix,scl,cross,show) %GEMANOVA - GEneralized Multiplicative ANOVA % Fits a GEMANOVA model to an N-way array of responses % X with F effects. % % INPUTS % The thi
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m gemanova.m

function model = gemanova(X,F,Fix,scl,cross,show) %GEMANOVA - GEneralized Multiplicative ANOVA % Fits a GEMANOVA model to an N-way array of responses % X with F effects. % % INPUTS % The thi
www.eeworm.com/read/363491/9949218

m 2danalyticalforpowerflow_1m.m

D=7.5e-4; %required from the paper "Ner mothod to calculate mode conversion %coefficients in SI multimode optical fibers",2003 A=1.37; %second order multiplicative factor in the series expansion o
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m analyticalforpowerflowequation2.m

D=7.5e-4; %required from the paper "Ner mothod to calculate mode conversion %coefficients in SI multimode optical fibers",2003 A=1.37; %second order multiplicative factor in the series expansion o
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m trans2_m.m

% Approximation of uncertain transfer function by unstructured % multiplicative perturbation % hold off omega = logspace(1,4,100); % % first-order fit Wm = nd2sys([0.3803 60.8973],[1.0000 599.5
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m trans1_m.m

% Approximation of uncertain transfer function by unstructured % multiplicative perturbation % hold off omega = logspace(1,4,100); % % first-order fit Wm = nd2sys([0.3877 25.6011],[1.0000 246.3
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c invert.c

/* mpz_invert (inv, x, n). Find multiplicative inverse of X in Z(N). If X has an inverse, return non-zero and store inverse in INVERSE, otherwise, return 0 and put garbage in INVERSE. Copyrigh
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m mulassoqp.m

function beta = MULASSOqp(Y,X,lambda,varargin) % The LASSO based on Multiplicative updates, i.e. solving the problem % % beta = argmin{ min 0.5*||Y-beta X||_2^2+lambda*|beta|_1 } % % The algorithm is